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Article history: Received: 05 November, 2019 Accepted: 21 February, 2020 Online: 09 March, 2020 Recent years have seen a continued pattern of development in the cloud computing field. Numerous approaches to maximize file transfer capacity are still completely standing for use on cloud computing storage; however, they do not maximize the advantage of data migration scalability and elasticity in cloud storage. One potential problem is that elasticity takes time; however, the scalability attributes that have not been fully exploited include multicore chips and parallelization that can further be leveraged to enhance the overall data transfer performance and efficiency. In that regard, considerable effort has been directed to multiprocessors. Such systems involve a plurality of processors or functioning units capable of independent operation to process separate tasks in parallel. Nevertheless, the penalization is complicated when a task requires several resources or signals to proceed with meaningful computation. Thus, accommodating equitable priority among tasks further complicates operations. In this paper, we propose a parallel server to cloud storage transfer system in which parallelism method can only be utilized in case of transferring a large number of files and applied in order to increase the transfer throughput. The data is transmitted into several chunks via TCP network within the same period slot in a single data path which indicates dataflow on parallelism. Our target in this system is that increasing number of processors and the problem size will simultaneously maintain the efficiency of the data transfer system. The proposed model is based on the combination of dynamic segmentation, CRS, AES, and hashing. In summary, the proposed model shows the potential to enhance the performance by increasing the data transferability. The performance of the proposed model will be measured with the help of comparing the average execution time with the number of processors and speedup of the entire parallel system.

Performance Evaluation and Examination of Data Transfer: A Review of Parallel Migration in Cloud Storage